Comparison and Analysis of Dynamic Measurement Error Correction Methods
In the process of product testing,due to the error of the testing equipment itself and the professional quality of the tester and other factors,there will be a certain deviation between the measured value and the actual value.At present,the common way to solve this problem is to remove the measurement error by certain algorithm means.In view of the shortcomings that the current error correction methods are generally not suitable for the dynamic measurement error correction,this paper studies the dynamic er-ror correction methods based on Bayesian,Grey model and Kalman filter,and compares the application of these methods,so as to provide ideas for selecting a reasonable error correction model and solving the error problem of measurement data.
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